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IntroductionData and Methodology

ResultsConclusions

Fertility Limits on Local Politicians in India

S. AnukritiBoston College

Abhishek ChakravartyUniversity of Essex

September 5, 2014

S. Anukriti, A. Chakravarty Fertility Limits on Local Politicians

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ResultsConclusions

MotivationContributionsBackground

Motivation

India is the world’s second most populous country and houses athird of the world’s poorest 1.2 billion citizens. Consequently,fertility reduction is atop the policy agenda.

Based on the recommendations of the Committee on Population set

up by the National Development Council in 1992, several Indianstates have enacted legislations that disbar individuals with morethan two children from contesting panchayat and/or municipalelections.

This paper examines the fertility and sex ratio impacts of this novelpolicy experiment that imposes fertility limits on political candidates.Our results point towards a novel source of demographic influence:political leaders.


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ResultsConclusions


Contributions

Our paper contributes to two distinct literatures: on the effects of leaders’ characteristics on followers’ behaviors and on determinantsof fertility and sex ratios in high-son preference countries.

The socioeconomic characteristics of individuals in positions of authority exert considerable influence on followers’ behaviors andoutcomes (Fernandez et al. (2004), Bettinger and Long (2005),Olivetta et al. (2013), Bassi and Rasul (2014)). Exposure totelevision and specific social content affects viewers’ fertility rates(Jensen and Oster (2009), Ferrara (2012)).

Recent work highlights the causal relationship between fertilitydecline and rising sex ratios in societies with son preference(Ebenstein (2010), Anukriti (2014), Jayachandran (2014)). Weaugment this literature by investigating a new source of fertilitydecline that has an unintended effect on sex ratios.


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ResultsConclusions


Background

Starting in 1992, eleven Indian states have imposed the two-childnorm for a few years. Four states revoked it in 2005, but it remainsin effect in seven states. In all cases, a one year grace-period wasprovided.

Haryana and MP explicitly mention two living children. AP, Orissa,and Rajasthan do not distinguish between births and living children.In Rajasthan, twins are considered one birth and a still-birth is notcounted, while in MP the District Collector has discretion over theseevents. Children given up for adoption are counted towards thetwo-child limit in all states.

In most states, for a disqualification, a complaint has to be filed withthe appropriate adjudicating authority, except in Orissa (for villagecouncils) and MP where the competent authority can initiate action.


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ResultsConclusions


Timeline

Table: Timeline for Two-Child Norms across States

State Announced Grace Period In effect End

Rajasthan 1992 Apr 23, 1994 - Nov 27, 1995 Nov 27, 1995 -

Haryana 1994 Apr 21, 1994 - Apr 24, 1995 Apr 25, 1995 - Dec 31, 2004 Jul 21, 2006(retro. impl. Jan 1, 2005)

Andhra Pradesh 1994 May 30, 1994 - May 30, 1995 Jun 1995 -Orissa 1993/1994* Apr 1994 - Apr 21, 1995 Apr 22, 1995 -Himachal Pradesh 2000 Apr 18, 2000 - Apr 18, 2001 Apr 2001 - Apr 2005 Apr 5, 2005Madhya Pradesh 2000** Mar 29, 2000 - Jan 26, 2001 Jan 2001 - Nov 2005 Nov 20, 2005Chhattisgarh 2000 2000 - Jan 2001 Jan 2001- 2005 2005 (earliest mention)1

Maharashtra 2003*** Sep 21, 2002 - Sep 20, 2003 Sep 2003 -Uttarakhand (municipal only) 2002Gujarat 2005 Aug 2005 - Aug 11, 2006 Aug 11, 2006 -

Bihar (municipal only) Jan 2007 Feb 1, 2007 - Feb 1, 2008 Feb 1, 2008 -

*For district councils in 1993 and for village and block councils in 1994.**Notified on May 31, 2000. This created problems since people whose third child was born in Jan 2001 contested their disqualificationfor birth within 8 months of the new law.***In retrospective effect from Sep 21, 2002.


I d i D

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ResultsConclusions

DataEvent Study FrameworkDD RegressionDDD Regression

Data

We use repeated cross-sectional data from three rounds of theNational Family Health Survey (NFHS-1, 2, 3) and one round of theDistrict-Level Household Survey (DLHS-2). The survey years are1992-93, 1998-99, and 2005-06 for the NFHS and 2002-04 for theDLHS.

Each survey-round is representative at the state-level and includes acomplete retrospective birth history for every woman interviewed,containing information on the month and the year of child’s birth,birth order, and mother’s age at birth. We combine these birthhistories to construct an unbalanced woman-year panel

A woman enters the panel in her year of first marriage and exits inher year of survey. For consistency across rounds, we limit thesample to currently-married women in the 15-44 age-group at thetime of survey.


I t od ctio Data

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ResultsConclusions


Data

We drop women (i) who were married more than 20 years prior tothe survey to avoid imperfect recall, (ii) whose husband’s age wasbelow 15 or above 80 in the year of survey, and (iii) who have hadmore than ten children to prevent composition-bias. We also excludemothers who have had twins since multiple births are unplanned anddo not reflect parents’ preferences.

Our final sample comprises 511,542 women and 1,261,711 birthsfrom 18 states and covers the time period 1973-2006. Uttarakhand,Jharkhand, and Chhattisgarh were carved out from Uttar Pradesh(UP), Bihar, and MP in 2000. Since we do not have

district-identifiers for all rounds, we subsume these three new statesinto their parent states.

We define treatment based on the year of announcement, theearliest year when the law might have had an effect. This yields us4-13 post-announcement years to estimate the effects of the norm.


Introduction Data

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ResultsConclusions


States and Treated Years

Table: Treatment years, by state

State Treat st = 1 if year >

Rajasthan 1993Orissa 1993Haryana 1994Andhra Pradesh 1994Himachal Pradesh 2000

Madhya Pradesh (inc. Chhattisgarh) 2000Maharashtra 2002


Introduction Data

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ResultsConclusions


Event Study Framework

We first use the event-study framework to depict the evolution of thelikelihood that a woman has more than two living children in a givenyear. We plot the β k coefficients from the following regression for awoman i of age a in state s and year t :

Y isat =

5

k =−10

β k Treat s ,

t +k + X

i δ + γ s + θt + ψa + isat (1)

where Treat s ,t +k indicates k years from the announcement of the law instate s . The year of announcement is the omitted year.

γ s , θ

t , and ψ

a are fixed-effects for state, year, and woman’s age.We control for covariates X i : five categories each for a woman’s and herhusband’s years of schooling, indicators for religion (five categories),caste (four categories), and standard of living (three categories), urbanresidence, and indicators for year of interview.


Introduction Data

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ResultsConclusions


Event Study Graph

Figure: Likelihood of more than two living children, by year

NOTES: This figure plots the βk coefficients and their 95% confidence intervals (dashed lines) from estimating equation (1). Standarderrors are clustered by state-year. The first vertical line (at k = 0) indicates the year of announcement. The second vertical line indicates

the end of the one-year grace period. The sample is restricted to women in treatment states.


Introduction Data

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Data and MethodologyResults

Conclusions

Event Study FrameworkDD RegressionDDD Regression

DD Regression

We then estimate the following differences-in-differences type regressionspecification for a woman i of age a in state s and year t :

Y isat = α+ β 1Treat st + X

i δ + γ s + θt + ψa + ν s ∗ t + isat (2)

where Treat st is equal to one for women residing in the treated states if t > the year of announcement, and zero otherwise. We also control forstate-specific linear time trends (ν s ∗ t ).

We restrict the sample to women whose first two children are born before

the treatment is announced in their state. The key coefficient of interestis β 1, which measures the effect of two-child limits on our outcomesvariable which is an indicator for a third birth.


Introduction Data

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Conclusions

Event Study FrameworkDD RegressionDDD Regression

DDD Regression

It is likely that the norm affects second parity births for couples who haveone child at announcement. We estimate a triple differences-in-differencesversion of (2) by interacting Treat st with indicator (Girl i ) for whether thefirst child (born before treatment) is a girl:

Y isat = α+ β2Treat st ∗ Girl i + φTreat st + ωGirl i

+ X

i δ + γ s + θt + ψa + ν s ∗ t + τ s ∗ Girl i + isat (3)

The outcome variables are indicators for a second birth and, conditional

on birth, the likelihood that the child is male.In addition, we restrict the sample to women whose first child is bornbefore the year of treatment.


IntroductionDD Results

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Conclusions

DD ResultsDDD ResultsRobustness Checks

DD Results

Table: Effects on Third Births

Only treated states BB BG GG

Dep Var: 3rd birth = 1 (1) (2) (3) (4) (5) (6) (7)

Treat st -0.0200*** -0.0049* -0.0068** -0.0068*** -0.0033 -0.0050 -0.0069**

[0.0054] [0.0028] [0.0024] [0.0022] [0.0023] [0.0032] [0.0032]

N 2,899,022 2,880,757 1,059,213 1,059,213 773,470 1,442,666 664,621

Control group mean 0.080 0.080 0.098 0.098 0.072 0.078 0.091

Year FE x x x x x x xState FE x x x x x x xCovariates x x x x x xState-specific linear trends x x x x x x

Clustering State State State State-Year State State StateN (clusters) 18 18 7 224 18 18 18

NOTES: This table reports the coefficients of Treat st from specification (1). Each coefficient is from a separate regression. Thedependent variable is one if there is a third birth in a given year, and zero otherwise. The sample is restricted to women whose first twochildren were born before the law was announced in her state. Only years after the second birth are included. Other covariates compriseindicators for the year of survey, woman’s age, household’s religion (Hindu, Muslim, Sikh, Christian), caste (SC, ST, OBC), wealth (low

and high SLI), husband’s and wife’s years of schooling (5 categories each), and residence in an urban area. In columns (3)-(4), the sampleis restricted to women in treatment states. BB, BG, GG respectively indicate the sub-samples of women whose first two births were two

boys, one boy-one girl, and two girls. *** 1%, ** 5%, * 10%.


IntroductionD t d M th d l

DD Results

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Conclusions

DD ResultsDDD ResultsRobustness Checks

DDD Results

Table: Effects on second birth, by first child’s sex

2nd birth = 1 2nd birth is male

Upper-caste Lower-caste Upper-caste Lower-caste

(1) (2) (3) (4) (5) (6)

Treat st * First − born girl -0.0029** -0.0030** -0.0026 0.0073 0.0307*** -0.0017[0.0012] [0.0013] [0.0015] [0.0064] [0.0093] [0.0061]

Treat st -0.0041* -0.0044 -0.0037 0.0039 -0.0065 0.0054[0.0023] [0.0028] [0.0025] [0.0050] [0.0082] [0.0051]

First − born girl 0.0024*** 0.0023*** 0.0023** 0.0103*** 0.0070*** 0.0123***[0.0007] [0.0006] [0.0009] [0.0013] [0.0016] [0.0014]

N 4,088,203 1,587,439 2,500,764 329,905 126,712 203,193

NOTES: The sample is restricted to women whose first child was born before the law was announced in her state. The dependent variableis one if there is a second birth in a given year, and zero otherwise. Only years after the first birth are included. Columns (4)-(6) are

conditional on a second birth. We drop post-2002 observations for Haryana. Each coefficient is from a separate regression that includesstate-specific linear time trends, fixed effects for state, year, and the interaction between state indicators and first-born girl dummy.

Standard errors are in brackets and are clustered by state. Covariates comprise indicators for the year of survey, woman’s age, household’sreligion (Hindu, Muslim, Sikh, Christian), (except in columns 3 and 4) caste (SC, ST, OBC), wealth (low and high SLI), husband’s and

wife’s years of schooling (5 categories each), and residence in an urban area. Lower-caste refers to SC, ST, OBC households; Upper-castecomprises the rest. *** 1%, ** 5%, * 10%.


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DD Results


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Conclusions

DDD ResultsRobustness Checks

Other Checks

Table: Robustness Checks

Dep Var → 4th birth = 1 NFHS only Age at first marriage(1) (2) (3)

Treat st -0.0057 -0.006** 0.005

[0.0033] [0.002] [0.336]

N 1,631,630 876,382 62,401

Year FE x x xState FE x x x

Covariates x x xState-specific linear trends x x xNOTES: Each coefficient is from a separate regression. In Column (1), the specification is similar to that in Table 3. The dependent

variable is one if there is a fourth birth in a given year, and zero otherwise. The sample is restricted to women whose first three childrenwere born before the law was announced in her state. Only years after the third birth are included. In Column (2), the sample is restrictedto NFHS data. In Column (3), the sample is restricted to one observation per woman. Treat st is equal to one if a woman’s first marriagetook place after the law was announced in her state, and zero otherwise. Standard errors are in brackets and are clustered by state. Othercovariates comprise indicators for the year of survey, household’s religion (Hindu, Muslim, Sikh, Christian), caste (SC, ST, OBC), wealth

(low and high SLI), husband’s and wife’s years of schooling (5 categories each), and residence in an urban area. *** 1%, ** 5%, * 10%.



DD Results

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Conclusions


Household Characteristics

Table: Correlations between Law Announcements and Socioeconomic Variables

Coefficient of Treat st Std. Error

Dependent Variable (1) (2)

Urban 0.007 [0.009]SC -0.003 [0.002]ST 0.006 [0.005]OBC 0.007 [0.006]Hindu -0.005* [0.003]Muslim 0.001 [0.002]

Sikh 0.0005 [0.001]Christian -0.001 [0.002]Low SLI -0.001 [0.005]High SLI 0.002 [0.005]Wife’s years of schooling:

Zero -0.002 [0.003]5-10 years 0.001 [0.003]10-12 years 0.001 [0.002]12-15 years 0.002 [0.002]≥ 15 years -0.0001 [0.001]Husband’s years of schooling:

Zero -0.001 [0.002]5-10 years 0.00009 [0.002]10-12 years 0.001 [0.002]12-15 years 0.002 [0.003]≥ 15 years -0.001 [0.002]

N 5,969,325

NOTES: Each coefficient is from a separate regression that includes state and year fixed effects and state-specific linear time trends.Standard errors are in brackets and are clustered by state. *** 1%, ** 5%, * 10%.



DD ResultsDDD R lt

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Conclusions


Role Models or Eligibility?

A role model effect should be visible only after the norm comes intoeffect. We should not then see a fertility response in the graceperiod.

We should also not expect to see a role model effect immediatelyafter the norm comes into effect.

A role model effect may also arguably lead to reduced fertilityregardless of current family size and election eligibility considerations.



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gyResults

Conclusions

Conclusions

Our results thus far can be explained by the role-model effect as wellas the incentive effect for individuals aspiring to run for office in thefuture. The data from NFHS and DLHS do not allow us todistinguish between these two channels.

In ongoing work, we seek to exploit variation in the gender- andcaste-based reservation status of village councils as an exogenousshock to the “attainability” of these leadership positions.

Lastly, to the extent that women and low-caste households mighthave relatively less control over their fertility decisions, these lawsmay have unintended consequences for the political representation of socioeconomically disadvantaged groups who have relatively higherfertility.

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